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BEIJING, Jan 19 (Reuters) - Chinese drugmakers rushed to make anti-fever medicines and other treatments for COVID-19 on Thursday, after President Xi Jinping said he was worried about an influx of holiday travellers to rural areas ill-equipped to deal with sudden outbreaks.

Xi's comments come just over a month after his government abruptly axed his strict "zero-COVID" controls that had largely shielded China's 1.4 billion people from the disease for three years but sparked widespread protests in late November.

As travel ramps up during the busy Lunar New Year holiday season, as many as 36,000 people could die each day from the disease, according to the latest figures from independent British-based forecasting firm Airfinity.

China said last Saturday that nearly 60,000 people with COVID died in hospitals between Dec. 8 and Jan. 12 - a roughly ten-fold increase from previous disclosures.

However, that number excludes those who die at home, and some doctors in China have said they are discouraged from putting COVID on death certificates. Health experts say China's official figures likely do not reflect the true toll of the virus.

"Based on the reports of hospitals being overwhelmed and long queues outside funeral homes, we might estimate that a larger number of COVID deaths have occurred so far, maybe more than 600,000 rather than just 60,000," said Ben Cowling, an epidemiologist at Hong Kong University.

China's chaotic exit from a regime of mass lockdowns, travel restrictions and frequent COVID testing, has also prompted a run on drugs as people fend for themselves against the disease.

To meet soaring demand, drugmakers in China are ramping up operations to triple their capacity to make key fever and cough medicines, the state-run China Daily reported on Thursday.

China has relied on domestic vaccines to combat the pandemic, eschewing foreign-made ones which some studies have suggested are more effective, while other foreign treatments for COVID-19 have been hard to come by in China.

Pfizer's (PFE.N) COVID-19 anti-viral drug Paxlovid is available in China but has been very difficult to obtain through official channels, according to media reports and personal accounts. Merck & Co's (MRK.N) antiviral treatment molnupiravir has also been approved for use but is not yet widely available.

Those particularly vulnerable to the virus are the elderly, many of whom are not fully vaccinated and now face exposure as millions of urban workers travel to home towns to reunite with families for Lunar New Year holidays that officially start on Jan. 21.

Before COVID first emerged in the central Chinese city of Wuhan in late 2019, the holiday was known as the greatest annual migration of people anywhere on the planet.

"China's COVID prevention and control is still in a time of stress, but the light is ahead, persistence is victory," Xi said on Wednesday in a holiday greetings message carried by state broadcaster CCTV.

"I am most worried about the rural areas and farmers. Medical facilities are relatively weak in rural areas, thus prevention is difficult and the task is arduous," Xi said, adding that the elderly were a top priority.

Several Chinese cities are set to celebrate the New Year with massive displays of fireworks after local governments reversed bans on their sale in recent years.

Hangzhou, Kunming, Zhengzhou, and Changsha - which all have populations above 10 million - will allow the sale of fireworks, according to the Yicai business magazine.

Airfinity on Wednesday estimated 62 million people could be infected with the virus between Jan. 13-27 and that COVID-related deaths could peak at 36,000 a day on Jan. 26, up sharply from previous forecasts.

"Our forecast estimates a significant burden on China's healthcare system for the next fortnight and it is likely that many treatable patients could die due to overcrowded hospitals and lack of care," Airfinity's analytics director Matt Linley said.

Reporting by Bernard Orr, Martin Quin Pollard and the Beijing newsroom; Writing by John Geddie; Editing by Neil Fullick

Our Standards: The Thomson Reuters Trust Principles.

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China races to make COVID drugs as Xi fears rural outbreaks

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BEIJING, Jan 19 (Reuters) - Chinese drugmakers rushed to make anti-fever medicines and other treatments for COVID-19 on Thursday, after President Xi Jinping said he was worried about an influx of holiday travellers to rural areas ill-equipped to deal with sudden outbreaks.

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Chinese pray for health in Lunar New Year as COVID death toll rises

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China rang in the Lunar New Year on Sunday with its people praying for health after three years of stress and financial hardship under the pandemic, as officials reported almost 13,000 new deaths caused by the virus between January 13 and 19.

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Tracking Coronavirus in China: Latest Case Count

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See the latest charts of coronavirus cases and deaths in China.

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( Xinhua) 09:29, January 22, 2023

Wang Hao, crew member of train No.G9480, decorates a train car with Spring Festival ornaments before the train leaves Bozhou South Railway Station in Bozhou, east China's Anhui Province, Jan. 13, 2023. (Xinhua/Zhang Duan)

BEIJING, Jan. 21 (Xinhua) -- Chinese Lunar New Year is just around the corner, and as Chinese people flock back home for family reunions, what is the country's COVID-19 situation at present?

With medical services returning to normal and COVID response measures in place at all levels and in all sorts of venues, a safe and sound Spring Festival holiday is expected, according to officials and health experts.

RETURNING TO NORMAL

The treatment of COVID-19 patients is smooth and orderly as day-to-day diagnoses and treatments gradually resume, Guo Yanhong, director of the Health Emergency Response Office of the National Health Commission, said at a press conference of the State Council joint COVID-19 prevention and control mechanism on Thursday.

The number of severe COVID-19 cases in hospitals nationwide reached its peak on Jan. 5 and has dropped by 44.3 percent as of Tuesday, Guo said.

She added that the numbers of patients in fever clinics and emergency rooms peaked on Dec. 23 and Jan. 2, respectively, and have decreased by 94 percent and 44 percent as of Tuesday.

COVID patient numbers have been declining in day-to-day medical services centers, with 99.5 percent of outpatients and 85 percent of hospitalized patients nationwide being non-COVID-19 patients on Tuesday, Guo said.

As for the newly detected XAY.2 variant of the coronavirus, Chang Zhaorui, a research fellow at the Chinese Center for Disease Control and Prevention, said it is yet to be detected in China. Chang warned that personal protection measures are still necessary, and that inbound travelers who have COVID-19 symptoms should monitor their health properly and seek medical treatment when necessary.

Rural doctors visit villagers to provide medical support in Jiwei Township of Huayuan County, Xiangxi Tujia and Miao Autonomous Prefecture in central China's Hunan Province, Dec. 29, 2022. (Photo by Jian Biao/Xinhua)

ENHANCING GRASSROOTS AND RURAL RESPONSE

Community-level and rural clinics are gearing up their COVID response measures as the Spring Festival travel rush is in full swing.

Guo Yanhong said that 98.8 percent of township-level clinics and community health centers across the country have set up fever clinics, which are required to maintain normal operations during the holidays.

Chinese health authorities have also provided community-level medical facilities with the necessary equipment. Jiao Yahui, head of the Bureau of Medical Administration under the NHC, said that 1.17 million finger-clip oximeters have been distributed to village clinics across the country.

As of Thursday, community-level medical institutions in China have been equipped with nearly 2.48 million finger-clip oximeters and 191,000 oxygen generators.

Moreover, 31 provinces and the Xinjiang Production and Construction Corps have established a special contact system to reach key groups in a timely manner and address their needs, as efforts are being made to distribute more medical supplies to rural areas and send more medical personnel to communities, said Mao Dezhi, deputy head of the Department of Rural Cooperative Economy under the Ministry of Agriculture and Rural Affairs.

Bullet train drivers prepare to work in Luoyang, central China's Henan Province, Jan. 21, 2023. People from various sectors stick to their posts on the eve of the Chinese Lunar New Year, or Spring Festival, which falls on Jan. 22. (Photo by Zhang Yixi/Xinhua)

SAFE HOLIDAYS

"The travel rush this year is the first of its kind since the country's COVID-19 prevention and control work entered a new stage, with a notable recovery of passenger flows," said Zhou Min, a Ministry of Transport (MOT) official.

To reduce infection risks, the MOT has stepped up efforts to enforce ventilation and sanitation measures at transport hubs and strengthen the protection and management of relevant personnel.

The ministry is also raising the quality of transport services and its capacity to ensure safer trips, with measures such as promoting contactless ticket purchases and ticket-checking services.

It is estimated that over 60 percent of trips during this year's holiday season will be road trips. To ensure the safety of road travel, the MOT has taken targeted measures to maintain a well-sanitized, sterilized and ventilated environment at highway service stops with high traffic flows, and has set up special zones at eligible stops to treat travelers infected with COVID-19 or feeling ill, Zhou said.

Following China's latest anti-COVID protocols, local culture and tourism authorities are also making efforts to ensure both a sound COVID response and the smooth operations of cultural and tourism sites and activities.

Routine response measures should be implemented fully at relevant venues to minimize virus transmission risks, said Li Xiaoyong, a Ministry of Culture and Tourism official.

(Web editor: Xue Yanyan, Liang Jun)

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With COVID preparedness, China poised for safe, sound Spring Festival holiday

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( Xinhua) 09:29, January 22, 2023 Wang Hao, crew member of train No.G9480, decorates a train car with Spring Festival ornaments before the train leaves Bozhou South Railway Station in Bozhou, east China's Anhui Province, Jan. 13, 2023. (Xinhua/Zhang Duan) BEIJING, Jan.

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Xi on COVID-19 response: light of hope right ahead

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Chinese President Xi Jinping on Wednesday talked about the country’s COVID-19 response, saying the light of hope is right ahead.

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When it comes to understanding the world of media politics, 85% of the current research on the
subject is concentrated in the US. In evaluating the interaction between media outlets and
citizens in China, Dr. Rory Truex said that his goal was to make this research more comparative
and broaden the discussion in the field. To this effect, the questions he posed were: in places
where the news is controlled, how do citizens perceive the news and how do they trust its
sources? How does this vary across the population?

Dr. Truex began by laying out the Chinese media landscape, which he divided into three sectors:
official papers, which are those under the direct control of the Chinese Communist Party (CCP),
commercial papers, which are those that are not strictly controlled and try to cultivate a brand of
independence, and foreign papers, which are those that face the lowest degree of control and are
accessible to Chinese citizens through Virtual Private Networks (VPNs).

He then moved on to discuss the existing theoretical arguments regarding media attitudes: the
naive model, the critical model, and the hostile media model. The naive model contends that
citizens are completely unaware of pro-government biases in media sources, while the critical
model is its diametric opposite, as it contends that citizens are extremely aware of progovernment
biases and therefore distrust media sources entirely. The hostile media model is an
amalgamation of the two, which displays a mix of ideologies who align with their respective
media sources.

The research design for the experiment was fairly straightforward. Dr. Truex targeted Chinese
netizens and conducted his survey online. He took three approaches to gauging their perceptions
of trustworthiness and media bias. The first approach was a direct question method where Dr.
Truex asked respondents to rate the trustworthiness and pro-government bias of fourteen
different newspaper sources across each media group. The second approach had an experimental
design where respondents read randomized sample news stories regarding different subjects,
alongside a randomized pairing of the paper's title. An example of this is that a story from the
People's Daily could be paired with the Epoch Times name. The third approach was in the form
of an open-ended question to respondents regarding their attitudes towards domestic media.

From this three-pronged survey design, Dr. Truex found some interesting results. The first is that
Chinese citizens are able to associate which news sources possess a certain degree of progovernment
bias, which pushes against the naive model. In fact, they seem to prefer this bias in
their news, which goes against the critical citizen argument. Another result from this survey is
that perceptions of bias in state-controlled outlets are nearly uniform across the ideological
spectrum, meaning that it is not just the regime skeptics who see the pro-regime bias in official
media, but also the regime supporters as well, thereby pushing against the hostile media model.

There were two reasons Dr. Truex provided as possible explanations for his findings. The first
was that people simply liked the CCP and therefore associated with official media. They saw a
pro-government bias in their media as a positive thing. This was reflected in several of the openended
responses as well, where people reflected a strong pro-government stance. The other
explanation given was that a strong identification with official sources could stem from an ability
to cherry-pick the truth from these sources. People could be aware of the inherent progovernment
bias and navigate through it to access the news and could trust these sources because
they are closer to the government.

A couple of drawbacks in Dr. Truex's study were highlighted by both himself and the audience at
the presentation. Dr. Truex pointed out that his selection of newspapers was arbitrary, and he
wished to rectify this in future research. Another drawback raised was the arbitrary attribution of
content to newspaper label, because in theory, a story from The New York Times could be
attributed to the People's Daily. According to Dr. Truex, respondents might have been put off and
confused during the survey in such cases. He concluded by saying that he expects to conduct
future research in Chinese media politics by studying whether citizens consume different news
topics in different news sources and why that is. Dr. Truex's presentation presented a perspective
on Chinese media politics that clashes with the conventional perception, furthering the
conversation on understanding Chinese citizens.

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Who Believes The People's Daily? Bias and Credibility in Authoritarian Media

# data[].attributes.attachment_link.description
When it comes to understanding the world of media politics, 85% of the current research on thesubject is concentrated in the US. In evaluating the interaction between media outlets andcitizens in China, Dr. Rory Truex said that his goal was to make this research more comparativeand broaden the discussion in the field.

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China suspending Social Media COVID policy Critics

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China suspending Social Media COVID policy Critics

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China suspends social media accounts of Covid policy critics

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The popular Sina Weibo social media platform said it had addressed 12,854 violations.

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COVID information dissemination using WeChat

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COVID information dissemination using WeChat

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The .gov means it's official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you're on a federal government site.

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

. 2021 Jul 16;23(7):e28563.

doi: 10.2196/28563.

Affiliations

¹ School of Management, Weifang Medical University, Weifang, Shandong, China.

² "Health Shandong" Collaborative Innovation Center for Severe Social Risk Prediction and Governance, Weifang, Shandong, China.

³ Collaborative Innovation Center of Social Risks Governance in Health, Shanghai, China.

Free PMC article

Zina Fan et al. J Med Internet Res.

Free PMC article

. 2021 Jul 16;23(7):e28563.

doi: 10.2196/28563.

Affiliations

¹ School of Management, Weifang Medical University, Weifang, Shandong, China.

² "Health Shandong" Collaborative Innovation Center for Severe Social Risk Prediction and Governance, Weifang, Shandong, China.

³ Collaborative Innovation Center of Social Risks Governance in Health, Shanghai, China.

Abstract

Background: The COVID-19 outbreak has tremendously impacted the world. The number of confirmed cases has continued to increase, causing damage to society and the economy worldwide. The public pays close attention to information on the pandemic and learns about the disease through various media outlets. The dissemination of comprehensive and accurate COVID-19 information that the public needs helps to educate people so they can take preventive measures.

Objective: This study aimed to examine the dissemination of COVID-19 information by analyzing the information released by the official WeChat account of the People's Daily during the pandemic. The most-read COVID-19 information in China was summarized, and the factors that influence information dissemination were studied to understand the characteristics that affect its dissemination. Moreover, this was conducted in order to identify how to effectively disseminate COVID-19 information and to provide suggestions on how to manage public opinion and information governance during a pandemic.

Methods: This was a retrospective study based on a WeChat official account. We collected all COVID-19-related information, starting with the first report about COVID-19 from the People's Daily and ending with the last piece of information about lifting the first-level emergency response in 34 Chinese provinces. A descriptive analysis was then conducted on this information, as well as on Qingbo Big Data's dissemination index. Multiple linear regression was utilized to study the factors that affected information dissemination based on various characteristics and the dissemination index.

Results: From January 19 to May 2, 2020, the People's Daily released 1984 pieces of information; 1621 were related to COVID-19, which mainly included headline news items, items with emotional content, and issues related to the pandemic's development. By analyzing the dissemination index, seven information dissemination peaks were discerned. Among the three dimensions of COVID-19 information-media salience, content, and format-eight factors affected the spread of COVID-19 information.

Conclusions: Different types of pandemic-related information have varying dissemination power. To effectively disseminate information and prevent the spread of COVID-19, we should identify the factors that affect this dissemination. We should then disseminate the types of information the public is most concerned about, use information to educate people to improve their health literacy, and improve public opinion and information governance.

Keywords: COVID-19; Chinese news; People's Daily; WeChat; information dissemination; media salience; public health and communication.

Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Cited by

Evaluation of the quality of COVID-19 guidance documents in anaesthesia using the Appraisal of Guidelines for Research and Evaluation II instrument.

O'Shaughnessy SM, Dimagli A, Kachulis B, Rahouma M, Demetres M, Govea N, Rong LQ. O'Shaughnessy SM, et al. Br J Anaesth. 2022 Dec;129(6):851-860. doi: 10.1016/j.bja.2022.09.008. Epub 2022 Sep 20. Br J Anaesth. 2022. PMID: 36273932 Free PMC article. Review.

References

Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, Zhao X, Huang B, Shi W, Lu R, Niu P, Zhan F, Ma X, Wang D, Xu W, Wu G, Gao GF, Tan W, China Novel Coronavirus Investigating and Research Team A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med. 2020 Feb 20;382(8):727-733. doi: 10.1056/NEJMoa2001017. http://europepmc.org/abstract/MED/31978945 - DOI - PMC - PubMed

Coronavirus Disease 2019 (COVID-19): Situation Report - 68. Geneva, Switzerland: World Health Organization; 2020. Mar 28, [2020-06-04]. http://apps.who.int/iris/bitstream/handle/10665/331614/nCoVsitrep28Mar20....

Wang PW, Lu WH, Ko NY, Chen YL, Li DJ, Chang YP, Yen CF. COVID-19-related information sources and the relationship with confidence in people coping with COVID-19: Facebook survey study in Taiwan. J Med Internet Res. 2020 Jun 05;22(6):e20021. doi: 10.2196/20021. https://www.jmir.org/2020/6/e20021/ - DOI - PMC - PubMed

Guo B. Comparison of news media under the background of epidemic situation. Journal Res Her. 2020;11(13):58-59. doi: 10.3969/j.issn.1674-8883.2020.13.032. - DOI

Divecha Z, Divney A, Ickovics J, Kershaw T. Tweeting about testing: Do low-income, parenting adolescents and young adults use new media technologies to communicate about sexual health? Perspect Sex Reprod Health. 2012 Sep;44(3):176-183. doi: 10.1363/4417612. http://europepmc.org/abstract/MED/22958662 - DOI - PMC - PubMed

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national center for biotechnology information

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COVID-19 Information Dissemination Using the WeChat Communication Index: Retrospective Analysis Study - PubMed

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Different types of pandemic-related information have varying dissemination power. To effectively disseminate information and prevent the spread of COVID-19, we should identify the factors that affect this dissemination. We should then disseminate the types of information the public is most concerned ...

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COVID19 Infodemic on Chinese Social Media

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COVID19 Infodemic on Chinese Social Media

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Monitoring Editor: Gunther Eysenbach

Reviewed by Xiaohui Wang and Liang Chen

Shuai Zhang, PhD, ¹ Wenjing Pian, PhD, ² Feicheng Ma, MSc, ¹ Zhenni Ni, BM, ¹ and Yunmei Liu, PhD ¹

Abstract

Background

The COVID-19 infodemic has been disseminating rapidly on social media and posing a significant threat to people's health and governance systems.

Objective

This study aimed to investigate and analyze posts related to COVID-19 misinformation on major Chinese social media platforms in order to characterize the COVID-19 infodemic.

Methods

We collected posts related to COVID-19 misinformation published on major Chinese social media platforms from January 20 to May 28, 2020, by using PythonToolkit. We used content analysis to identify the quantity and source of prevalent posts and topic modeling to cluster themes related to the COVID-19 infodemic. Furthermore, we explored the quantity, sources, and theme characteristics of the COVID-19 infodemic over time.

Results

The daily number of social media posts related to the COVID-19 infodemic was positively correlated with the daily number of newly confirmed ( r=0.672, P<.01) and newly suspected ( r=0.497, P<.01) COVID-19 cases. The COVID-19 infodemic showed a characteristic of gradual progress, which can be divided into 5 stages: incubation, outbreak, stalemate, control, and recovery. The sources of the COVID-19 infodemic can be divided into 5 types: chat platforms (1100/2745, 40.07%), video-sharing platforms (642/2745, 23.39%), news-sharing platforms (607/2745, 22.11%), health care platforms (239/2745, 8.71%), and Q&A platforms (157/2745, 5.72%), which slightly differed at each stage. The themes related to the COVID-19 infodemic were clustered into 8 categories: "conspiracy theories" (648/2745, 23.61%), "government response" (544/2745, 19.82%), "prevention action" (411/2745, 14.97%), "new cases" (365/2745, 13.30%), "transmission routes" (244/2745, 8.89%), "origin and nomenclature" (228/2745, 8.30%), "vaccines and medicines" (154/2745, 5.61%), and "symptoms and detection" (151/2745, 5.50%), which were prominently diverse at different stages. Additionally, the COVID-19 infodemic showed the characteristic of repeated fluctuations.

Conclusions

Our study found that the COVID-19 infodemic on Chinese social media was characterized by gradual progress, videoization, and repeated fluctuations. Furthermore, our findings suggest that the COVID-19 infodemic is paralleled to the propagation of the COVID-19 epidemic. We have tracked the COVID-19 infodemic across Chinese social media, providing critical new insights into the characteristics of the infodemic and pointing out opportunities for preventing and controlling the COVID-19 infodemic.

Keywords: COVID-19, infodemic, infodemiology, epidemic, misinformation, spread characteristics, social media, China, exploratory, dissemination

Introduction

Background

As the COVID-19 pandemic continued to develop, we experienced the parallel rise of the COVID-19 infodemic [ 1, 2]. This infodemic is a phenomenon of overabundance of information caused by COVID-19 misinformation, which has rapidly propagated on social media and attracted widespread attention from the government and health agencies during the ongoing pandemic [ 3, 4]. The infodemic has made the pandemic worse, harmed more people, and jeopardized the global health system's reach and sustainability [ 5, 6]. Thus, the World Health Organization (WHO) has called it a disease accompanying the COVID-19 epidemic [ 7].

The term "infodemic" is derived from a combination of the root words "information" and "epidemic" and was coined by Eysenbach in 2002 [ 8], when a SARS outbreak had emerged in the world. It was not until the WHO Director-General reintroduced the term "infodemic" at the Munich Security Conference on February 15, 2020, that it had begun to be used more widely, summarizing the challenges posed by COVID-19 misinformation to our society [ 9]. In this study, the term "infodemic" refers to an information abundance phenomenon wherein the lack of reliable, trustworthy, and accurate information associated with the COVID-19 epidemic has enabled COVID-19 misinformation to disseminate rapidly across a variety of social media platforms [ 10]. Thus, the COVID-19 infodemic is also called the COVID-19 misinformation epidemic [ 11].

Misinformation refers to a claim that is not supported by scientific evidence and expert opinion [ 12]. This definition explains that misinformation can act as an umbrella concept to explain different types of incorrect information, such as false information, fake news, misleading information, rumors, and anecdotal information, regardless of the degree of facticity and deception [ 13]. Research linking misinformation to epidemic diseases is emerging [ 14]. There have been multiple instances where misinformation has been correlated with negative public health outcomes, including the spread of Zika virus [ 15] and vaccine-preventable infectious diseases in many countries worldwide [ 16]. Another salient example is the COVID-19 pandemic. For instance, Nsoesie and Oladeji [ 17] investigated the impact of misinformation on public health during the COVID-19 pandemic. They found that COVID-19 misinformation prevented people from demonstrating effective health behaviors and weakened the public's trust in the health care system. Therefore, dealing with COVID-19 misinformation requires urgent attention.

The increasing global access of social media via mobile phones has led to an exponential increase in the generation of misinformation as well as the number of possible ways to obtain it, thus resulting in an infodemic. Infodemics have co-occurred with epidemics such as Ebola and Zika virus in the past [ 18, 19]. However, the COVID-19 infodemic is significantly different from the earlier ones. It has been reported as "the first true social-media infodemic" [ 20]. It is also the first infodemic to have been disseminated widely through social media and has significantly impacted public health [ 21]. By the beginning of 2020, more than 3.8 billion people used social media [ 22]. Moreover, social media is one of the most popular media for information dissemination and distribution, with 20%-87% usage surging during the crisis [ 23]. Recently, Oxford's Reuters Institute investigated the dissemination of misinformation and found that a majority (88%) of the misinformation about COVID-19 originated from social media [ 24]. In Italy, approximately 46,000 posts posted per day on social media in March 2020 were linked to COVID-19 misinformation [ 25].

In China, the COVID-19 infodemic was more serious [ 26]. Two-thirds of the Chinese population used social media, and approximately 87% of all users encountered relevant misinformation during the COVID-19 crisis [ 27]. Examples of such misinformation spread on Chinese social media include that compound Chinese medicine and Banlangen could cure COVID-19; consuming methanol, ethanol, or bleach could protect or cure COVID-19; pneumonia vaccines could protect against SARS-CoV-2; eating garlic could kill the virus; and 5G mobile network has spread COVID-19 [ 28]. Moreover, China was the first country to experience the COVID-19 infodemic [ 18]. In December 2019, the first case of COVID-19 was reported in China [ 29]. In subsequent weeks, the rapid spread of novel coronavirus caused increasing discussion among social media users. Countless unproven stories, advice, and therapies related to COVID-19 were prevalent and skyrocketed on Chinese social media platforms [ 30].

The COVID-19 infodemic is immensely concerning because all social media users can be affected by it, which poses a severe threat to public health [ 31]. A study showed that 5800 people were admitted to the hospital as a result of the COVID-19 misinformation disseminated on social media [ 32]. More seriously, the misinformation that consumption of neat alcohol can cure COVID-19 led to hundreds of deaths due to poisoning [ 33]. Moreover, the infodemic on social media can also lead to inappropriate actions by users and endanger the government and health agencies' efforts to manage COVID-19, inducing panic and xenophobia [ 2, 34].

Given the negative impact of the COVID-19 infodemic on social media, especially on Chinese social media, the government and health agencies need to assess the COVID-19 infodemic on Chinese social media. Therefore, in this study, we aimed to analyze the quantity, source, and theme characteristics of the COVID-19 infodemic by collecting posts related to COVID-19 misinformation on published on Chinese social media from January 20 to May 28, 2020. Specifically, we used content analysis to analyze the quantity and source of the COVID-19 infodemic. Then, we used topic modeling to analyze various themes of the infodemic. Finally, we explored the quantity, source, and theme characteristics of the COVID-19 infodemic over time.

Prior Works

Previous studies have investigated the distribution and themes of infodemics on social media in other countries. For example, Oyeyemi et al [ 35] used the Twitter search engine to collect posts about the Ebola virus from September 1 to 7, 2014. They found that 58.9% of the posts were identified as misinformation. Moreover, the study indicated that misinformation was rampant on social media and had a greater impact on users than did correct information. Similarly, Tran and Lee [ 36] investigated the propagation of the Ebola infodemic and found that misinformation was more widespread on social media than correct information. Glowacki et al [ 37] further collected posts about the Zika virus on the live Twitter chat initiated by the Centers for Disease Control and Prevention. They applied topic modeling and derived the following 10 topics relevant to the Zika epidemic: "virology of Zika," "spread," "consequences for infants," "promotion of the chat," "prevention and travel precautions," "education and testing for the virus," "consequences for pregnant women trying to conceive," "insect repellant," "sexual transmission," and "symptoms."

With the world's commitment to the fight against COVID-19, there has been active research in many areas, including social media and quantitative analyses. For example, Kouzy et al [ 11] assessed the source characteristics of the COVID-19 infodemic being spread on Twitter. They used descriptive statistics to analyze Twitter accounts and post characteristics and found that 66% of misinformation posts regarding the COVID-19 epidemic was posted by unverified individual or group accounts, and 19.2% were posted by verified Twitter users' accounts. Moreover, they indicated that the COVID-19 infodemic is being propagated at an alarming rate on social media. Another study by the COVID-19 Infodemic Observatory found that robots generated approximately 42% of the social media posts related to the pandemic, of which 40% were considered unreliable [ 38]. Similarly, the Bruno Kessler Foundation analyzed 112 million social media posts about COVID-19 information [ 26]. The results showed that 40% of this information was from unreliable sources [ 22]. At the same time, Moon et al [ 39] collected 200 of the most viewed Korean-language YouTube videos on COVID-19 published from January 1 to April 30, 2020. They found that 37.14% of the videos contained misinformation, and independent videos generated by the user showed the highest proportion of misinformation at 68.09%, whereas all government-generated videos were regarded as useful. Additionally, Naeem et al [ 23] selected 1225 pieces of misinformation about COVID-19 published in the English language on various social media platforms from January 1 to April 30, 2020, and coded the data using an open coding scheme. They concluded that the theme characteristics of the COVID-19 infodemic include "false claims," "half-backed conspiracy theories," "pseudoscientific therapies," "regarding the diagnosis," "treatment," "prevention," "origin," and "spread of the virus."

Objectives

An increasing number of studies have begun to highlight the COVID-19 infodemic on social media. However, attempts to characterize the spread of the COVID-19 infodemic on social media, especially on Chinese social media platforms, are currently lacking. Hence, in this study, we used content analysis and topic modeling to analyze the COVID-19 infodemic across Chinese social media platforms to gain new insights into the quantity, source, and theme characteristics of the infodemic over time and propose measures to contain the dissemination of misinformation during the COVID-19 infodemic.

Methods

Data Collection

The database for this study was obtained from Qingbo Big Data Agency [ 40], which covers data from almost all major Chinese social media platforms, such as WeChat, Weibo, and TikTok. The posts collected included microblogs, messages, or short articles shared on these social media platforms. Our search strategy to retrieve post data comprised of the following keywords in Chinese: "coronavirus," "2019-nCoV," "COVID-19," "corona," "new pneumonia," and "new crown." We used PythonToolkit to crawl the data searched using the abovementioned keywords from January 20 to May 28, 2020. The data collection process was as follows. First, we searched the Qingbo Big Data Agency to obtain the results page. Second, the web link crawler was initiated, and the title and URL fields of all web pages were collected. Third, these fields were stored in the url_list dataset of the MongoDB database. Fourth, the web page details crawler was launched, the post published time, source, and text fields of the details page were collected. Finally, these fields were stored in the info_list dataset of the MongoDB database. After data collection was completed, datasets url_list and info_list from the MongoDB database were exported. It should be noted that for video-sharing platforms, the textual description of the video was captured as the post data. Data collection began on January 20, 2020, when the Chinese State Council officially announced the COVID-19 epidemic as a public health emergency [ 31]. Data collection ended on May 28, 2020, when the National Health Commission of the People's Republic of China issued that the number of new confirmed cases and new suspected cases of COVID-19 in China was zero for the first time. This data collection period could reflect the overall spread of the COVID-19 infodemic on Chinese social media.

All data regarding COVID-19 posts were retrieved, and 723,216 posts were extracted in total. To improve the representativeness of data, we removed incomplete data from the fields and deleted text longer than 400 Chinese characters [ 41], thus obtaining data from a total of 143,197 posts. Because most of these posts were reposts, we only retained 19,188 of the original post data. We verified the authenticity of post data using the following 2 steps. First, we conduct fact-checking according to the authority organization, such as the National Health Commission of the People's Republic of China, the Chinese Center for Disease Control and Prevention, and the Cyberspace Administration of China. We only retained those posts that were judged to be fake and obtained data from 1729 posts. Next, two independent researchers reviewed and evaluated the remaining posts. One of them is a doctoral student in Library and Information Science, and the other has a bachelor's degree in Medicine. Discrepancies between the 2 researchers were resolved through mutual discussion. The Cohen kappa coefficient was used to analyze the interreviewer reliability for coding. Cohen's Kappa value for the 2 researchers was 0.79, suggesting substantial agreement between them [ 42]. Ultimately, we obtained 2745 posts related to COVID-19 misinformation as the final analysis sample for this study, which was the largest dataset the study team could obtain with the available resources. The post data was organized and stored chronologically, and the title, URL, post date, source, and text were recorded. Table 1 details the data format of the posts collected for the analysis.

Data Processing

We used Python (version 3.8.5) and SPSS software (version 25.0; IBM Corp) to perform all data processing and analyses. Time segmentation adapted from the practice of Zhao et al [ 18] was used to divide the period into 19 time segments (T ₁: January 20-26, 2020; T ₂: January 27 to February 2, 2020; T ₃: February 3-9, 2020; T ₄: February 10-16, 2020; T ₅: February 17-23, 2020; T ₆: February 24 to Mar 1, 2020; T ₇: March 2-8, 2020; T ₈: March 9-15, 2020; T ₉: March 16-22, 2020; T ₁₀: March 23-29, 2020; T ₁₁: March 30 to April 5, 2020; T ₁₂: April 6-12, 2020; T ₁₃: April 13-19, 2020; T ₁₄: April 20-26, 2020; T ₁₅: April 27 to May 3, 2020; T ₁₆: May 4-10, 2020; T ₁₇: May 11-17, 2020; T ₁₈: May 18-24, 2020; and T ₁₉: May 25-28, 2020). Among these segments, the last time segment is 4 days long, and the other time segments are 7 days long each, with the total period spanning 130 days.

Based on the classification of social media websites by the CNNIC (China Internet Network Information Center) [ 43], the sources of posts were categorized into 5 types: chat platforms, video-sharing platforms, news-sharing platforms, health care platforms, and Q&A platforms. The chat platforms included WeChat, Weibo, and QQ. The video-sharing platforms included TikTok, Kuaishou, and Pear Video. The news-sharing platforms included Toutiao, Sina, and Tencent. The health care platforms included DXY.cn, Haodf.com, and Chunyu Yisheng. The Q&A platforms include Zhihu, Douban, and Jianshu (see a full list of Chinese social media types and major social media sites in Multimedia Appendix 1).

The "jieba" package in Python was used to segment post text. We limited the parts of speech of the post text to 9 categories ("n," "nr," "ns," "nt," "eng," "v," "vn," "vs," and "d"). We adapted the method described by Medford et al [ 29] to merge synonyms into a unified form (eg, "disinfectant powder" and "disinfectant water" into "disinfectants" and "suspense of business" and "termination of business" into "close down"). The Gensim package in Python was used to perform latent Dirichlet allocation (LDA) model. A post contains only one dominant topic. We used different numbers of topics to iteratively train multiple LDA models to maximize the topic coherence score. After more than 10 tests, the results with the highest coherence score in the use of the LDA model with 8 topics were selected. Each topic contains 15 words adhering to convention and is manually tagged with a theme.

Data Analysis

We explored characteristics of the COVID-19 infodemic on Chinese social media from the perspective of quantity, source, and theme. From the perspective of quantity, we counted the daily number of posts and obtained the number of newly confirmed cases and suspected cases each day from the official website of the Chinese Center for Disease Control and Prevention. We performed Pearson correlation analysis to explore the relationship between the daily number of posts with the number of newly confirmed cases and suspected cases per day. Moreover, we calculated the maximum, minimum, upper quartile, lower quartile, and median number of posts in each time segment, and we visualized them to intuitively evaluate the characteristics of post propagation. From the perspective of source and theme, we calculated the sources and themes of posts based on the number of occurrences. Additionally, we visualized the number of sources of posts in each time segment to analyze the source characteristics of the COVID-19 infodemic. We then created a visualization of the time segment of themes of posts to assess the change in themes over time.

Results

Quantity Characteristics

Figure 1 shows the daily number of posts related to the COVID-19 misinformation on Chinese social media that was published from January 20 to May 28, 2020. The maximum number of posts published in a day was 105, whereas the minimum number was 3 (mean 21.12, SD 17.35). Pearson correlation analysis shows that the daily number of posts related to the COVID-19 infodemic was positively correlated with the daily number of newly confirmed ( r=0.672, P<.01) and newly suspected ( r=0.497, P<.01) COVID-19 cases in China. In other words, the more posts related to the COVID-19 misinformation that were published per day, the greater was the severity of the COVID-19 epidemic, and vice versa.

We used a box plot to describe the spread of social media posts according to different time segments ( Figure 2 ). We found that the posts presented a spread characteristic indicating gradual progress. That is, the number of posts first increases slowly with the time segment, then concentrates on the burst, and then moderates gradually as the time segments continue to advance. Furthermore, the COVID-19 infodemic on Chinese social media can be divided into 5 periods (see Table 2 ). During the incubation period (Stage A: T ₁-T ₂), the number of posts showed slow growth, with the mean and median values of approximately 20 per day. Then, the number of posts rapidly increased during the outbreak period (Stage B: T3-T4), and the mean and median values soared to approximately 50 per day. During the stalemate period (Stage C: T ₅-T ₈), the number of posts remained at a high level, and the mean and median values were approximately 30 per day. During the control period (Stage D: T ₉-T ₁₅), the number of posts dropped significantly, with mean and median values of approximately 14 per day. Finally, the number of posts has decreased sluggishly in the recovery period (Stage E: T ₁₆-T ₁₉), and the mean and median values remained at approximately 7 per day.

Table 2

Time segment

T ₁-T ₂

T ₃-T ₄

T ₅-T ₈

T ₉-T ₁₅

T ₁₆-T ₁₉

Source Characteristics

Of the posts related to the COVID-19 misinformation that were classified ( Figure 3 ), chat platforms (1100/ 2745, 40.07%) represented the largest source of the COVID‐19 infodemic, followed by video-sharing platforms (642/ 2745, 23.39%) and news-sharing platforms (607/2745, 22.11%). The proportions of health care platforms (239/2745, 8.71%) and Q&A platforms (157/2745, 5.72%) were relatively small.

We visualized the number of sources of posts in each time segment ( Figure 4 ). Chat, video-sharing, and news-sharing platforms were the main sources for the spread of posts during the incubation period (T ₁-T ₂). Then, the posts began to spread toward the health care and Q&A platforms during the outbreak period (T ₃-T ₄). Thereafter, the posts were broadly spread on all social media platforms and were maintained at a high level during the stalemate period (T ₅-T ₈). During the control period (T ₉-T ₁₅), the spread of the posts on chat and video-sharing platforms alternately increased and decreased, whereas the spread of posts on news-sharing, health care, and Q&A platforms evidently declined. Finally, the spread of posts on chat platforms also gradually decreased during the recovery period (T ₁₆-T ₁₉), and the spread on other social media platforms dropped sharply and remained at a low level.

Theme Characteristics

Topic modeling identified 8 different themes, which are illustrated in Figure 5 . The 15 keywords that contributed to each theme with their potential theme labels are shown in Table 3 . Based on LDA analysis, we obtained a specific theme for each post. The popularity of each theme was determined based on the proportion of posts in each theme considering the overall post data. The most common primary theme was "conspiracy theories" (648/2745, 23.61%), which included topics such as "Academician Zhong Nanshan did not wear a mask for rounds," "Academician Li Lanjuan helped her son sell medicines," "Dr. Li Wenliang danced before his death," and "Wuhan Huoshenshan was designed by the Japanese." The second most common theme was "government response" (544/2745, 19.82%), which included the following topics: "The city would be closed down at 2:00 PM on January 25, 2020, in Xinyang, Henan province;" "Wuhan gas stations would be closed;" and "Jingzhou, Hubei Province, would suspend issuing permits for leaving Hubei Province." Thereafter, the themes discussed were "prevention action" (411/2745, 14.97%) and "new cases" (365/2745, 13.30%), which included topics such as "Wearing multi-layer masks can prevent the virus," "Smoking vinegar can prevent the virus," "Six promoters of Wuhan Zhongbai Supermarket were confirmed with novel coronavirus pneumonia," and "More than 20,000 new confirmed close contacts in Qingdao." The other common themes included "transmission route" (244/2745, 8.89%) as well as "origin and nomenclature" (228/2745, 8.30%). These themes included the following topics: "Catkins can transmit COVID-19," "COVID-19 is a biological weapon," and "COVID-19 was made by the laboratory." Other themes included "vaccines and medicines" (154/2745, 5.61%) as well as "symptoms and detection" (151/2745, 5.50%), which included topics such as "CT image is used as the latest standard for judging the diagnosis of COVID-19," "Hold your breath for 10 seconds to test whether you are infected with the virus," "The first COVID-19 vaccine was successfully developed and injected," and "Hydroxychloroquine and chloroquine are specific drugs for COVID-19."

Table 3

Theme labels

Keywords contributing to topic model

Origin and nomenclature (#0)

COVID-19, SARS, Corona, SARI, host animals, bat, pangolin, variation, pestilence, influenza, the natural world, man-made, biological weapon, laboratory, patient zero

Transmission routes (#3)

5G, seafood, aerosol, catkin, mosquito, paper money, tap water, aquatic product, public toilet, sweater, air conditioner, pet dog, freshwater fish, salmon, subway ticket

Prevention action (#4)

prevention, face mask, disinfectant, alcohol, N95, chlorine, liquor, onion, garlic, vinegar, tea, smoke, strawberries, eyedrops, balm

New cases (#1)

infection, case, confirmed, suspected, patient, isolation, hospital, community, airport, hotel, school, nursing home, student, old people, infant

Symptoms and detection (#7)

detection, test positive, cough, fever, outpatient, computed tomography, lung, blood type, plasma, antibody, diagnostic kit, self-test, suffocation, asymptomatic, expectoration

Government response (#5)

lockdown, road closure, close down, health code, living material, trip, network, transportation, traffic control, home quarantine, traffic permitting, work resumption, school opens, customs office, inbound

Vaccines and medicines (#6)

vaccine, chloroquine, remdesivir, azithromycin, Shuanghuanglian oral liquid, Lianhua Qingwen capsule, Banlangen, oseltamivir, azithromycin, aspirin, Angong Niuhuang Wan, traditional Chinese medicine, Bacillus Calmette-Guerin, toxic strain, Chinese fevervine herb

Conspiracy theories (#2)

Zhong Nanshan, Li Lanjuan, Li Wenliang, Leishenshan, Huoshenshan, Donald John Trump, modular hospital, doctors, nurses, online course, blood donation, suicide, escape, medical corps, cleaner, Red Cross Society

The "Pyecharts" package in Python was used to draw a heat map of themes according to the time segments ( Figure 6 ). We found that different hot themes were discussed at each stage of the COVID-19 infodemic. The theme "origin and nomenclature" was discussed from the start of the incubation period (T ₁-T ₂). The themes "government response," "new cases," and "transmission routes" were debated on social media during the outbreak period (T ₃-T ₄). The discussion of "conspiracy theories" and "symptoms and detection" increased significantly in the stalemate period (T ₅-T ₈). During the control period (T ₉-T ₁₅), the discussion of "prevention action" was concentrated. Subsequently, the theme "vaccines and medicines" was the focus of discussion on social media during the recovery period (T ₁₆-T ₁₉).

We further found that the COVID-19 infodemic presented a spread characteristic of repeated fluctuations across time segments. As shown in Figure 6 , each theme is repeated in the time segment, and the theme discussion rate gradually decreases. For example, the theme "government response" not only appeared in the time segment T ₂-T ₆, but it was also spread in the time segment T ₈-T ₉, T ₁₁-T ₁₂, T ₁₄, and T ₁₇. Moreover, we determined the number of repeated posts for each theme in the time segment (see Table 4 ) and calculated that the total ratio of repeated posts to be 0.2849 (782/2745), which means that 28.49% of the posts were posted repeatedly in various time segment. This once again verified the spread characteristic of the COVID-19 infodemic that fluctuates repeatedly across time segments. Additionally, the repetition percentage of the themes "conspiracy theories" (198/648, 30.6%), "new cases" (110/365, 30.1%), and "prevention action" (121/411, 29.4%) were particularly high, followed by the themes "government response" (157/544, 28.9%), "origin and nomenclature" (63/228, 27.6%), and "transmission routes" (64/244, 26.2%). The repetition percentage of the themes "vaccines and medicines" (37/154, 24%) and "symptoms and detection" (32/151, 21.2%), however, were relatively low. Differences in repetition among the themes were analyzed by analysis of variance and post hoc analysis, which revealed significant differences in the repetition of themes (F=2.402, P=.02). The post hoc tests showed that the theme of "conspiracy theories" was more significant than the theme "symptoms and detection" ( P<.01) and the theme "vaccines and medicines" ( P=.04). However, no significant differences were observed between the themes "symptoms and detection" and "vaccines and medicines" ( P=.29).

Discussion

Principal Findings

To our knowledge, this study is the first of its kind to analyze posts related to the COVID-19 infodemic on Chinese social media platforms. Previous studies about the COVID-19 infodemic on social media have been mainly qualitative in nature [ 1, 7]. In this study, we analyzed 2745 posts about the COVID-19 infodemic published on Chinese social media platforms between January 20, 2020, and May 28, 2020, which had more than 100 million views cumulatively. We analyzed various characteristics of the COVID-19 infodemic on Chinese social media from the perspective of quantity, source, and theme, to provide decision support for government and health agencies. Below, we discuss 5 key findings of our study that are noteworthy.

First, it was interesting to find that the daily number of posts related to the COVID-19 misinformation on Chinese social media was positively correlated with the daily number of newly confirmed ( r=0.672, P<.01) and newly suspected ( r=0.497, P<.01) COVID-19 cases in China. This finding indicated that the COVID-19 infodemic paralleled the propagation of the COVID-19 outbreak in China. Our finding is similar to previous studies on posts related to the H7N9 outbreak on Weibo, which showed a positive correlation between the daily number of posts published and the daily number of deaths due to H7N9 infection [ 44].

Second, we found that the COVID-19 infodemic was characterized by gradual progress, which can be divided into 5 stages. During the incubation period (T ₁-T ₂), since COVID-19 cases were only reported in Wuhan, the COVID-19 infodemic showed slow growth. Subsequently, the COVID-19 infodemic increased rapidly during the outbreak period (T ₃-T ₄), as the COVID-19 began to spread across China, causing a mass of public discussion on social media. Thereafter, as the number of COVID-19 cases continued to increase, the COVID-19 infodemic maintained a high level in the stalemate period (T ₅-T ₈). During the control period (T ₉-T ₁₅), because of the remarkable decrease in the number of COVID-19 cases, the COVID-19 infodemic also significantly declined. Finally, during the recovery period (T ₁₆-T ₁₉), the COVID-19 infodemic generally decreased, as the number of COVID-19 cases dropped constantly.

Third, our study found that the COVID-19 infodemic was characterized by videoization. Sources of the COVID-19 infodemic can be divided into 5 types (ie, chat, video-sharing, news-sharing, health care, and Q&A platforms). Among these, video-sharing platforms (23.38%) emerged as the second-largest source after chat platforms. The dissemination mode of "seeing is believing" was subduing public awareness of the COVID-19 epidemic. Moreover, it may be a new spread characteristic for the infodemic. Additionally, we found that the COVID-19 infodemic was more prevalent on chat, video-sharing, and news-sharing platforms than on health care and Q&A platforms. One possible explanation for this difference is that on chat, video-sharing, and news-sharing platforms, users tend to post personal experiences more centrally, which may often be inaccurate, whereas more professional expertise may likely be shared on health care and Q&A platforms.

Fourth, we found that the themes of the COVID-19 infodemic changed with different spread characteristics across stages. Users posted a large number of posts about "origin and nomenclature" in the incubation period (T ₁-T ₂) and gradually changed to themes such as "government response," "new cases," and "transmission routes" in the outbreak period (T ₃-T ₄). Subsequently, the themes changed to "conspiracy theories" and "symptoms and detection" in the stalemate period (T ₅-T ₈), and then progressively concentrated on the themes "prevention action" in the control period (T ₉-T ₁₅). Finally, in the recover period (T ₁₆-T ₁₉), the theme changed to "vaccines and medicines." This phenomenon is in line with the characteristic that public opinion online would result in a change in themes in a given period [ 45, 46].

Fifth, our study found that the COVID-19 infodemic showed the characteristic of repeated fluctuations. It indicated that the governance of the COVID-19 infodemic on social media is a "protracted-war." Prior study has also pointed out that the effect of refuting misinformation usually lasts for less than a week [ 47, 48]. Moreover, we found that the repetition rate of the COVID-19 infodemic themes also differed according to the time segments. The theme "conspiracy theories" was significantly more thrive than the themes "symptoms and detection" and "vaccines and medicines." One possible explanation is that the theme "conspiracy theories" comprised more uncertain knowledge than the themes "symptoms and detection" and "vaccines and medicines." Therefore, users are more inclined to repeat posts of the theme "conspiracy theories."

With regard to the practical implications to curb the COVID-19 infodemic on Chinese social media, our findings suggest that the government and health agencies should manage the infodemic in a stage-wise manner and take more efforts to disseminate accurate and professional information via social media to ameliorate the spread of falsehoods. For instance, expert-approved or peer-reviewed videos are expected to provide credible health information. Furthermore, government and health agencies must pay close attention to the spread of the infodemic on video-sharing platforms. Third, they should coordinate with social media companies to establish long-term systems for the prevention and control of the infodemic. For example, social media platforms can curb the repeated dissemination of COVID-19 misinformation by setting alert labels for repeated misinformation and regularly pushing corrective information to users. Additionally, social media may offer novel opportunities for the government and health agencies to assess and predict the trend of epidemic outbreaks.

Limitations

There are some limitations to this study. First, we targeted posts on Chinese social media; thus, our conclusions may not be applied to social media platforms in other countries, such as Twitter. Second, we collected and analyzed only a relevant subset of all posts about the COVID-19 infodemic, which inevitably introduces some selection bias. Third, as the COVID-19 infodemic continues to disseminate, we should extend the time and expand the data volume to provide the government and health agencies with a more comprehensive prevention and control response. Additionally, our analyses of the repetition of infodemic are still inadequate, and we will further explore this interesting phenomenon in a future study.

Conclusions

Our study found that the COVID-19 infodemic on Chinese social media was characterized by gradual progress, videoization, and repeated fluctuations. Our findings suggest that the COVID-19 infodemic paralleled the propagation of the COVID-19 epidemic. These findings can help the government and health agencies collaborate with major social media companies to develop targeted measures to prevent and control the COVID-19 infodemic on Chinese social media. Moreover, social media offers a novel opportunity for the government and health agencies to surveil epidemic outbreaks.

Acknowledgments

SZ acknowledges financial support from the National Natural Science Foundation of China (No. 71420107026).

Abbreviations

LDA

latent Dirichlet allocation

WHO

World Health Organization

Appendix

Multimedia Appendix 1

Different types of Chinese social media and major social media platforms.

Footnotes

Contributed by

Authors' Contributions: SZ and FM conceptualized the study design. SZ and NN collected and analyzed the data. SZ, FM, and WP interpreted the results and wrote the manuscript. SZ, FM, WP, and YL revised the manuscript. All authors have read and approved the final draft of the manuscript.

Conflicts of Interest: None declared.

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Articles from JMIR Public Health and Surveillance are provided here courtesy of JMIR Publications Inc.

# data[].attributes.attachment_link.title
Characterizing the COVID-19 Infodemic on Chinese Social Media: Exploratory Study

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Monitoring Editor: Gunther Eysenbach Reviewed by Xiaohui Wang and Liang Chen Shuai Zhang, PhD, 1 Wenjing Pian, PhD, 2 Feicheng Ma, MSc, 1 Zhenni Ni, BM, 1 and Yunmei Liu, PhD 1 The COVID-19 infodemic has been disseminating rapidly on social media and posing a significant threat to people's health and governance systems. This study aimed to investigate and analyze posts related to COVID-19 misinformation on major Chinese social media platforms in order to characterize the COVID-19 infodemic.

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COVID outbreak in China infects 80% of population

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The coronavirus has affected 80% of the population in China. Nearly 60,000 people with the virus have died roughly a month after China dismantled its zero-COVID policy.

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Eight in 10 people in China have now been infected with Covid-19, a prominent government scientist has claimed.

Wu Zunyou, the chief epidemiologist of China's Center for Disease Control and Prevention, said on Saturday - the eve of the Lunar New Year - that the present "wave of epidemic has already infected about 80% of the people" in the country of 1.4 billion people.

His claim came amid concerns the travel rush that takes place around the Lunar New Year holiday period - sometimes dubbed the 'world's largest human migration' - could spread the virus to the countryside and cause a second wave of infections.

But Wu, speaking on his personal social media account, said this scenario was unlikely as so many people had already been infected.

"In the next two to three months, the possibility of a large-scale Covid-19 rebound or a second wave of infections across the country is very small," Wu said.

China's Ministry of Transport estimates over 2 billion passenger trips will take place during the 40-day Lunar New Year season as people across the country return to their hometowns for family reunions for the first time without domestic travel restrictions since the start of the pandemic over three years ago.

Chinese state broadcaster CCTV reported on Sunday that more than 26 million passenger trips were taken on the eve of Lunar New Year.

That is only half of the number of travelers from the same day in 2019, prior to the Covid-19 pandemic, but 50.8% higher than 2022, it said.

More than 4.1 million people traveled by train and 756,000 people by air for holiday reunions on the day before the start of Lunar New Year, CCTV added.

The country's road transport system registered more than 20 million passenger trips on the same day, 55.1 % higher than the number from 2022.

As of Friday, China's transport system had handled over 560 million passenger trips in the first 15 days of the 40-day ongoing Spring Festival travel rush via rail, highway, water, and air, up 47.9% from the same period last year, according to CCTV.

Skepticism over infection figures

Previously, research by the National School of Development at Peking University had suggested more than 900 million people, or 64% of the Chinese population, were "likely" to have been infected with Covid-19 as of January 11.

The university's research also suggested the epidemic peaked across much of the country on December 20, about 13 days after China began easing its Covid restrictions.

By the end of December, infections had peaked everywhere in the country, according to the research.

Chinese health authorities have made similar claims, saying on Thursday that visits to clinics for fever and Covid hospitalizations in China have declined since their peaks in late December and early January respectively.

According to the authorities, the number of Covid patients needing critical care in hospitals has also peaked.

Nearly 60,000 people with Covid died in Chinese hospitals between December 8 and January 12 after China abruptly scraped its "zero-Covid" policies, according to government data.

However, the World Health Organization's executive director for health emergencies Mike Ryan suggested earlier this month that the numbers released by China "under-represent the true impact of the disease" in terms of hospital and ICU admissions, as well as deaths.

According to an internal estimate from the nation's top health officials, in the first 20 days of December almost 250 million people in China may have caught Covid. If correct, the estimate - which CNN cannot independently confirm - would account for roughly 18% of China's population.

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China says 80% of population have had Covid-19, as millions travel for Lunar New Year | CNN

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Eight in 10 people in China have now been infected with Covid-19, a prominent government scientist has claimed. Wu Zunyou, the chief epidemiologist of China's Center for Disease Control and Prevention, said on Saturday - the eve of the Lunar New Year - that the present "wave of epidemic has already infected about 80% of the people" in the country of 1.4 billion people.

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James Glanz, Mara Hvistendahl and Agnes ChangFeb. 15, 2023

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Rough estimates suggest that between 1 and 1.5 million people have died since China ended “zero Covid” — far more than the official count.

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In China, from 3 January 2020 to 6:30pm CEST, 16 August 2023, there have been 99,302,177 confirmed cases of COVID-19 with 121,628 deaths, reported to WHO. As of 23 March 2023, a total of 3,515,872,818 vaccine doses have been administered.

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China: WHO Coronavirus Disease (COVID-19) Dashboard With Vaccination Data

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China Coronavirus(COVID-19) statistics. Total and daily confirmed cases and deaths.

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could not find any articles from "local" news regarding the COVID scene in China. Looked at "The Day" and "New England Fisherman" without any results of China's COVID policy

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could not find any articles from "local" news regarding the COVID scene in China. Looked at "The Day" and "New England Fisherman" without any results of China's COVID policy